Non-equilibrium diffusion limit of the compressible Navier-Stokes-Fourier-P1 approximation model at low Mach number: general initial data case

In this paper, we investigate the non-equilibrium diffusion limit of the compressible Navier-Stokes-Fourier-P1 (NSF-P1) approximation model at low Mach number, which arises in radiation hydrodynamics, with general initial data and a parameter $\delta \in [0,2]$ describing the intensity of scatting effect. In previous literature, only $\delta =2$ and well-prepared initial data case to the NSF-P1 model was considered. Here we prove that, for partial general initial data and $\delta =2$, this model converges to the system of low Mach number heat-conducting viscous flows coupled with a diffusion equation as the parameter $\epsilon \rightarrow 0$. Compared to the classical NSF system, the NSF-P1 model has additional new singular structures caused by the radiation pressure. To handle these structures, we construct an equivalent pressure and an equivalent velocity to balance the order of singularity and establish the uniform estimates of solutions by designating appropriate weighted norms and carrying out delicate energy analysis. We then obtain the convergence of the pressure and velocity from the local energy decay of the equivalent pressure and equivalent velocity. We also briefly discuss the variations of the limit equations as the scattering intensity changes, i.e., $\delta \in (0,2)$. We find that, with the weakening of scattering intensity, the ``diffusion property" of radiation intensity gradually weakens. Furthermore, when the scattering effect is sufficiently weak ($\delta =0$), we can obtain the singular limits of the NSF-P1 model with general initial data. To our best knowledge, this is the first result on the influence of scattering intensity in the non-equilibrium diffusion limit of the NSF-P1 model.Comment: 30 page

DOVIS: an implementation for high-throughput virtual screening using AutoDock

<p>Abstract</p> <p>Background</p> <p>Molecular-docking-based virtual screening is an important tool in drug discovery that is used to significantly reduce the number of possible chemical compounds to be investigated. In addition to the selection of a sound docking strategy with appropriate scoring functions, another technical challenge is to <it>in silico </it>screen millions of compounds in a reasonable time. To meet this challenge, it is necessary to use high performance computing (HPC) platforms and techniques. However, the development of an integrated HPC system that makes efficient use of its elements is not trivial.</p> <p>Results</p> <p>We have developed an application termed DOVIS that uses AutoDock (version 3) as the docking engine and runs in parallel on a Linux cluster. DOVIS can efficiently dock large numbers (millions) of small molecules (ligands) to a receptor, screening 500 to 1,000 compounds per processor per day. Furthermore, in DOVIS, the docking session is fully integrated and automated in that the inputs are specified via a graphical user interface, the calculations are fully integrated with a Linux cluster queuing system for parallel processing, and the results can be visualized and queried.</p> <p>Conclusion</p> <p>DOVIS removes most of the complexities and organizational problems associated with large-scale high-throughput virtual screening, and provides a convenient and efficient solution for AutoDock users to use this software in a Linux cluster platform.</p

Development of Quantitative Structure−Binding Affinity Relationship Models Based on Novel Geometrical Chemical Descriptors of the Protein−Ligand Interfaces

Novel geometrical chemical descriptors have been derived based on the computational geometry of protein-ligand interfaces and Pauling atomic electronegativities (EN). Delaunay tessellation has been applied to a diverse set of 517 X-ray characterized protein-ligand complexes yielding a unique collection of interfacial nearest neighbor atomic quadruplets for each complex. Each quadruplet composition was characterized by a single descriptor calculated as the sum of the EN values for the four participating atom types. We termed these simple descriptors generated from atomic EN values and derived with the Delaunay Tessellation the ENTess descriptors and used them in the variable selection k-Nearest Neighbor quantitative structure-binding affinity relationship (QSBR) studies of 264 diverse protein-ligand complexes with known binding constants. 24 complexes with chemically dissimilar ligands were set aside as an independent validation set, and the remaining dataset of 240 complexes was divided into multiple training and test sets. The best models were characterized by the leave-one-out cross-validated correlation coefficient q2 as high as 0.66 for the training set and the correlation coefficient R2 as high as 0.83 for the test set. High predictive power of these models was confirmed independently by applying them to the validation set of 24 complexes yielding R2 as high as 0.85. We conclude that QSBR models built with the ENTess descriptors can be instrumental for predicting the binding affinity of receptor-ligand complexes

Explore postgraduate biomedical engineering course integration between medical signal processing and drug development: example for drug development in brain disease

Medical signal processing is a compulsory course in our university’s undergraduate biomedical engineering programme. Recently, application of medical signal processing in supporting new drug development has emerged as a promising strategy in neurosciences. Here, we discuss the curriculum reformation in biomedical signal processing course in the context of drug development and application in central nervous system, with a particular emphasis in knowledge integration

HIV-1 protease function and structure studies with the simplicial neighborhood analysis of protein packing method

The Simplicial Neighborhood Analysis of Protein Packing (SNAPP) method was used to predict the effect of mutagenesis on the enzymatic activity of the HIV-1 protease (HIVP). SNAPP relies on a four-body statistical scoring function derived from the analysis of spatially nearest neighbor residue compositional preferences in a diverse and representative subset of protein structures from the Protein Data Bank. The method was applied to the analysis of HIVP mutants with residue substitutions in the hydrophobic core as well as at the interface between the two protease monomers. Both wild type and tethered structures were employed in the calculations. We obtained a strong correlation, with R2 as high as 0.96, between ΔSNAPP score (i.e., the difference in SNAPP scores between wild type and mutant proteins) and the protease catalytic activity for tethered structures. A weaker but significant correlation was also obtained for non-tethered structures as well. Our analysis identified residues both in the hydrophobic core and at the dimeric interface (DI) that are very important for the protease function. This study demonstrates a potential utility of the SNAPP method for rational design of mutagenesis studies and protein engineering

Chemometric Analysis of Ligand Receptor Complementarity: Identifying Complementary Ligands Based on Receptor Information (CoLiBRI)

We have developed a novel structure-based approach to search for Complimentary Ligands Based on Receptor Information (CoLiBRI). CoLiBRI is based on the representation of both receptor binding sites and their respective ligands in a space of universal chemical descriptors. The binding site atoms involved in the interaction with ligands are identified by the means of computational geometry technique known as Delaunay tessellation as applied to x-ray characterized ligand-receptor complexes. TAE/RECON1 multiple chemical descriptors are calculated independently for each ligand as well as for its active site atoms. The representation of both ligands and active sites using chemical descriptors allows the application of well-known chemometric techniques in order to correlate chemical similarities between active sites and their respective ligands. From these calculations, we have established a protocol to map patterns of nearest neighbor active site vectors in a multidimensional TAE/RECON space onto those of their complementary ligands, and vice versa. This protocol affords the prediction of a virtual complementary ligand vector in the ligand chemical space from the position of a known active site vector. This prediction is followed by chemical similarity calculations between this virtual ligand vector and those calculated for molecules in a chemical database to identify real compounds most similar to the virtual ligand. Consequently, the knowledge of the receptor active site structure affords straightforward and efficient identification of its complementary ligands in large databases of chemical compounds using rapid chemical similarity searches. Conversely, starting from the ligand chemical structure, one may identify possible complementary receptor cavities as well. We have applied the CoLiBRI approach to a dataset of 800 x-ray characterized ligand receptor complexes in the PDBbind database2. Using a k nearest neighbor (kNN) pattern recognition approach and variable selection, we have shown that knowledge of the active site structure affords identification of its complimentary ligand among the top 1% of a large chemical database in over 90% of all test active sites when a binding site of the same protein family was present in the training set. In the case where test receptors are highly dissimilar and not present among the receptor families in the training set, the prediction accuracy is decreased; however CoLiBRI was still able to quickly eliminate 75% of the chemical database as improbable ligands. The CoLiBRI approach provides an efficient prescreening tool for large chemical databases prior to traditional, yet much more computationally intensive, three-dimensional docking approaches

A Novel Automated Lazy Learning QSAR (ALL-QSAR) Approach: Method Development, Applications, and Virtual Screening of Chemical Databases Using Validated ALL-QSAR Models

A novel Automated Lazy Learning Quantitative Structure-Activity Relationship (ALL-QSAR) modeling approach has been developed based on the lazy learning theory. The activity of a test compound is predicted from locally weighted linear regression model using chemical descriptors and biological activity of the training set compounds most chemically similar to this test compound. The weights with which training set compounds are included in the regression depend on the similarity of those compounds to a test compound. We have applied the ALL-QSAR method to several experimental chemical datasets including 48 anticonvulsant agents with known ED50 values, 48 dopamine D1-receptor antagonists with known competitive binding affinities (Ki), and a Tetrahymena pyriformis dataset containing 250 phenolic compounds with toxicity IGC50 values. When applied to database screening, models developed for anticonvulsant agents identified several known anticonvulsant compounds that were not only absent in the training set but highly chemically dissimilar to the training set compounds. This initial success indicates that ALL-QSAR can be further exploited as a general tool for accurate bioactivity prediction and database screening in drug design and discovery. Due to its local nature, the ALL-QSAR approach appears to be especially well suited for the development of highly predictive models for the sparse or unevenly distributed datasets